• Helped create technologies like Delta Lake (https://delta.io) and Apache Spark's Structured Streaming to make the lives of data engineers easier across the industry

• Apache Spark committer since January 2017

• Contributions to Apache Spark MLlib and SparkSQL around linear algebra, statistics

• Worked in setting up Databricks' centralized data pipeline.

• Gave guest lectures on Spark at UC Berkeley, UCLA, and Istanbul Sehir University

• Worked with high profile customers as a Solutions Architect (1 month rotation) to make them successful on Databricks and Spark

Staff Software Engineer / Engineering Manager

• Leads the data ingestion team, where the mission is to bring as much data into the Databricks Lakehouse as seamlessly as possible

Worked on the open source project Apache Spark.

Made contributions to the Machine Learning Library (MLlib) for Spark.

Wrote performance tests for MLlib to monitor performance regressions and optimize when necessary.

Fully funded RA-ship. Developed a new method called Sampled Stochastic Subgradient (SaSS), that

combines two optimization frameworks: Randomized Linear Algebra (RLA) and Stochastic Gradient

Descent (SGD), as a best-of-both-worlds algorithm. The algorithm shows state-of-the-art performance

in the medium precision regime.